(hydra_cfg: DictConfig)
| 20 | |
| 21 | @hydra.main(version_base="1.2", config_path="../configs", config_name="eval") |
| 22 | def main(hydra_cfg: DictConfig): |
| 23 | # setup_debug(hydra_cfg.debug) |
| 24 | logger = logging.getLogger("relpose-dist") |
| 25 | |
| 26 | all_eval_models: DictConfig = hydra_cfg.eval_models # see configs/evaluation/relpose-distance.yaml |
| 27 | all_eval_datasets: DictConfig = hydra_cfg.eval_datasets # see configs/evaluation/relpose-distance.yaml |
| 28 | all_data_info: DictConfig = hydra_cfg.data # see configs/data |
| 29 | all_model_info: DictConfig = hydra_cfg.model # see configs/model |
| 30 | |
| 31 | for idx_model, model_keyname in enumerate(all_eval_models, start=1): |
| 32 | # 0.1 look up model config from configs/model, decide the model name (to save) |
| 33 | if model_keyname not in all_model_info: |
| 34 | raise ValueError(f"Unknown model in global data information: {model_keyname}") |
| 35 | model_info = all_model_info[model_keyname] |
| 36 | |
| 37 | # 0.2 load the model |
| 38 | model = hydra.utils.instantiate(model_info.cfg).to(hydra_cfg.device) |
| 39 | logger.info(f"[{idx_model}/{len(all_eval_models)}] Loaded Model {model_keyname} from {model_info.cfg.pretrained_model_name_or_path if hasattr(model_info.cfg, 'pretrained_model_name_or_path') else '???'}") |
| 40 | |
| 41 | # 0.3 route the correct infer function for the model |
| 42 | infer_func_cfg = model_info.get( |
| 43 | "infer_cameras_c2w", |
| 44 | DictConfig({ |
| 45 | '_target_': f'interfaces.{model_keyname}.infer_cameras_c2w', |
| 46 | '_partial_': True, |
| 47 | }) |
| 48 | ) |
| 49 | infer_cameras_c2w = hydra.utils.instantiate(infer_func_cfg) |
| 50 | |
| 51 | model_logger = logging.getLogger(f"relpose-dist-{model_keyname}") |
| 52 | for idx_dataset, dataset_name in enumerate(all_eval_datasets, start=1): |
| 53 | # 1. look up dataset config from configs/data, decide the dataset name |
| 54 | if dataset_name not in all_data_info: |
| 55 | raise ValueError(f"Unknown dataset: {dataset_name}") |
| 56 | dataset_info = all_data_info[dataset_name] |
| 57 | |
| 58 | # 2. get the sequence list |
| 59 | seq_list = get_all_sequences(dataset_info) |
| 60 | output_root = osp.join(hydra_cfg.output_dir, model_keyname, dataset_name) |
| 61 | os.makedirs(output_root, exist_ok=True) |
| 62 | |
| 63 | # 3. infer for each sequence |
| 64 | model = model.eval() |
| 65 | model_logger.info(f"[{idx_dataset}/{len(all_eval_datasets)}] Infering relpose(c2w) on {dataset_name} dataset..., output to {osp.relpath(output_root, hydra_cfg.work_dir)}") |
| 66 | |
| 67 | results = [] |
| 68 | tbar = tqdm(seq_list, desc=f"[{dataset_name} eval]") |
| 69 | for seq in tbar: |
| 70 | try: |
| 71 | # 4.1 list all images of this sequence |
| 72 | filelist = list_imgs_a_sequence(dataset_info, seq) |
| 73 | filelist = filelist[:: hydra_cfg.pose_eval_stride] |
| 74 | |
| 75 | # 4.2 real inference |
| 76 | # pr_poses: c2w poses, (N, 3, 4), in torch |
| 77 | # pr_intrs: focals + pps, (N, 3, 3), in numpy |
| 78 | pr_poses, pr_intrs = infer_cameras_c2w(filelist, model, hydra_cfg) |
| 79 | pred_traj = get_tum_poses(pr_poses) |
no test coverage detected